Weakly Supervised Segmentation by Tensor Graph Learning for Whole Slide Images

Abstract

Semantic segmentation of whole slide images (WSIs) helps pathologists identify lesions and cancerous nests. However, training fully supervised segmentation networks usually requires plenty of pixel-level annotations, which consume lots of time and human efforts. Coming from tissues of different patients with large amounts of pixels, WSIs exhibit various patterns, resulting in intra-class heterogeneity and inter-class homogeneity. Meanwhile, most existing methods for WSIs focus on extracting a certain type of features, neglecting the relations between different features and their joint effect on segmentation. Therefore, we propose a novel weakly supervised network based on tensor graphs (WSNTG) for WSI segmentation. Using only sparse point annotations, it efficiently segments WSIs by superpixel-wise classification and credible node reweighting. To deal with the variability of WSIs, the proposed network represents multiple hand-crafted features and hierarchical features yielded by a pretrained Convolutional Neural Network (CNN). Particularly, it learns over the semi-labeled tensor graphs constructed on the hierarchical features to exploit nonlinear data structures and associations. It gains robustness via the tensor-graph Laplacian of the hand-crafted features superimposed on the segmentation loss. We evaluated WSNTG on two WSI datasets, DigestPath2019 and SICAPV2. Results show that it outperforms many fully supervised and weakly supervised methods with minimal point annotations in WSI segmentation. The codes are published at https://github.com/zqh369/WSNTG.